






"""simple docstring"""
import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: List[Any] ,     __snake_case: Dict ,     __snake_case: Optional[int] ,     __snake_case: Union[str, Any]  )    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    TapasConfig.from_json_file(__snake_case  )
       # set absolute/relative position embeddings parameter
       __magic_name__      				=    reset_position_index_per_cell

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              __magic_name__      				=    TapasForQuestionAnswering(config=__snake_case  )
       elif task == "WTQ":
              # run_task_main.py hparams
              __magic_name__      				=    4
              __magic_name__      				=    True
              # hparam_utils.py hparams
              __magic_name__      				=    0.664694
              __magic_name__      				=    0.207951
              __magic_name__      				=    0.121194
              __magic_name__      				=    True
              __magic_name__      				=    True
              __magic_name__      				=    False
              __magic_name__      				=    0.0352513

              __magic_name__      				=    TapasForQuestionAnswering(config=__snake_case  )
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              __magic_name__      				=    4
              __magic_name__      				=    False
              # hparam_utils.py hparams
              __magic_name__      				=    36.4519
              __magic_name__      				=    0.903421
              __magic_name__      				=    222.088
              __magic_name__      				=    True
              __magic_name__      				=    True
              __magic_name__      				=    True
              __magic_name__      				=    0.763141

              __magic_name__      				=    TapasForQuestionAnswering(config=__snake_case  )
       elif task == "TABFACT":
              __magic_name__      				=    TapasForSequenceClassification(config=__snake_case  )
       elif task == "MLM":
              __magic_name__      				=    TapasForMaskedLM(config=__snake_case  )
       elif task == "INTERMEDIATE_PRETRAINING":
              __magic_name__      				=    TapasModel(config=__snake_case  )
       else:
              raise ValueError(F"""Task {task} not supported."""  )

       print(F"""Building PyTorch model from configuration: {config}"""  )
       # Load weights from tf checkpoint
       load_tf_weights_in_tapas(__snake_case ,     __snake_case ,     __snake_case  )

       # Save pytorch-model (weights and configuration)
       print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
       model.save_pretrained(__snake_case  )

       # Save tokenizer files
       print(F"""Save tokenizer files to {pytorch_dump_path}"""  )
       __magic_name__      				=    TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + 'vocab.txt' ,     model_max_length=5_1_2  )
       tokenizer.save_pretrained(__snake_case  )

       print('Used relative position embeddings:' ,     model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
 snake_case : Tuple     		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
 )
 parser.add_argument(
     """--reset_position_index_per_cell""",
     default=False,
     action="""store_true""",
     help="""Whether to use relative position embeddings or not. Defaults to True.""",
 )
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--tapas_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained TAPAS model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 snake_case : Dict     		=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case : int     		=		{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case : str     		=		["""NllbTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case : Dict     		=		["""NllbTokenizerFast"""]


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_nllb import NllbTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_nllb_fast import NllbTokenizerFast

else:
 import sys

 snake_case : Dict     		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)